The invention generally relates to a system for managing health care and, in particular, to a system for filtering electrical signals generated by a voltage provided to a test strip for testing physiological fluid in a test meter.
Diabetes is a chronic disease that requires continued monitoring and controlling of health parameters such as blood glucose levels, medication, nutritional condition, and weight and exercise data. Also, patients with cardiovascular disease may require the monitoring of cholesterol levels. Because of the chronic nature of such diseases, health parameters must be measured on a continual periodic basis by the patients themselves outside a clinical setting.
Conventionally electronic meters are used to measure glucose from a test strip when a patient applies blood to it. The test strip may be designed to include two or more electrodes connectable with a meter and used to develop an electrical pathway through the sample. The meter is then able to determine the electrical characteristics of the sample and through known correlations may be able to deduce the concentration of a particular analyte in the sample.
Conventional methods for the measurement of blood glucose response current use an analog to digital (A/D) converter. The A/D converter samples the voltage, which is generated from an electronic circuit, connected to the test strip. The current generated in the strip is very low, only a few microamperes and the resolution of the signal should be as high as possible to achieve a high resolution blood glucose signal. Because of all kinds of noise from the environment, measuring a very small signal with a high resolution by nature leads to technical difficulties. Such noise sources may include signals from light switches, mobile phones, electrostatic discharge pulses, power supplies connected to mains, etc. These disturbances add to the signal and falsify the measurement result.
Conventionally, there are hardware and software solutions to solving the noise problem. One example of a hardware solution may be to integrate the signal by, for example, using an R-C (resistor-capacitor) combination circuit. Such a circuit may be used to smooth the signal and improve the signal to noise ratio. However, the application of such a circuit introduces delays and reduces bandwidth.
An example of a software solution is to sample the signal as often as possible and then calculate an average of all values sampled. The principle is similar to the hardware integration, however it “weights” every sample the same. Accordingly, a very short glitch, which may occur just when the sampling starts, is given too much weight thereby overshadowing other signal changes during the conversion period and those signal changes may be ignored. Nevertheless, such a software solution usually reduces the noise to an acceptable “quasi noise-free” value, especially when it has to eliminate “white noise” effects. This principle however is limited when there is not sufficient time to take enough A/D readings or the noise is not “white noise”. As more readings are being averaged, the final result will be more accurate. However, taking too many readings takes a significant amount of time. Also for the software solution to calculate the average produces a phase shifting of the real signal versus the average result. For example, taking readings from a constant changing signal like the blood glucose transient over a period of, for example, 200 ms will give an average value that is available 100 ms delayed and also corresponds to the mean value over the last 200 ms.
In blood glucose monitoring, the software averaging algorithm, described above, alone may not be sufficient to filter noise and provide a preferred level of performance because a “glitch” or noise disturbance, which does not accurately represent the electrical characteristics of the sample, may be so large as to dominate the smaller measured values which are closely representative of the true electrical characteristics of the sample. Accordingly, there is a need for an alternative filtering method which is tailored to eliminate “glitches” or noise disturbances like those generated from light switches, electrostatic discharge, switching power supplies, and also from the meter electronics itself, which may have microcontrollers with external address and data bus devices with switching frequencies in the megahertz (MHz) range. Thus, there is a need for a filtering algorithm which analyzes those values before a noise disturbance, which may only take a few microseconds, and may cause only one or two A/D readings to be wrong (usually extremely high or low) and identifies these “wrong” or “extreme” readings and filters them out before the average is calculated over the remaining value. Such a system would have a significant advantage over conventional methods, because less A/D readings would be required to come to a “quasi noise-free” result and thus much less time would be needed to take a sufficient number of samples. Such a methodology would also have the effect of reducing the phase delay.
The techniques herein below extend to those embodiments which fall within the scope of the appended claims, regardless of whether they accomplish one or more of the above-mentioned needs.